
"""simple docstring"""



from __future__ import annotations

import math


def 			_UpperCamelCase						(    _A       ,   _A   )     ->	float:





    """simple docstring"""

    _UpperCAmelCase        = u
    for i in range(1       ,   _A   ):
        _UpperCAmelCase        = temp * (u - i)
    return temp


def 			_UpperCamelCase						(    )     ->	None:





    """simple docstring"""

    _UpperCAmelCase        = int(input("""enter the numbers of values: """   )   )
    _UpperCAmelCase        = []
    for _ in range(_A   ):
        y.append([]   )
    for i in range(_A   ):
        for j in range(_A   ):
            y[i].append(_A   )
            _UpperCAmelCase        = 0

    print("""enter the values of parameters in a list: """   )
    _UpperCAmelCase        = list(map(_A       ,   input().split()   )   )

    print("""enter the values of corresponding parameters: """   )
    for i in range(_A   ):
        _UpperCAmelCase        = float(input()   )

    _UpperCAmelCase        = int(input("""enter the value to interpolate: """   )   )
    _UpperCAmelCase        = (value - x[0]) / (x[1] - x[0])

    # for calculating forward difference table

    for i in range(1       ,   _A   ):
        for j in range(n - i   ):
            _UpperCAmelCase        = y[j + 1][i - 1] - y[j][i - 1]

    _UpperCAmelCase        = y[0][0]
    for i in range(1       ,   _A   ):
        summ += (ucal(_A       ,   _A   ) * y[0][i]) / math.factorial(_A   )

    print(F"""the value at {value} is {summ}"""   )


if __name__ == "__main__":
 main()

"""simple docstring"""



import argparse

import torch
from huggingface_hub import hf_hub_download

from transformers import AutoTokenizer, RobertaPreLayerNormConfig, RobertaPreLayerNormForMaskedLM
from transformers.utils import logging


logging.set_verbosity_info()
a       :						Any       		=		logging.get_logger(__name__)


def 			_UpperCamelCase						(    _A       ,   _A   )     ->	int:





    """simple docstring"""

    _UpperCAmelCase        = RobertaPreLayerNormConfig.from_pretrained(
        _A       ,   architectures=["""RobertaPreLayerNormForMaskedLM"""]   )

    # convert state_dict
    _UpperCAmelCase        = torch.load(hf_hub_download(repo_id=_A       ,   filename="""pytorch_model.bin"""   )   )
    _UpperCAmelCase        = {}
    for tensor_key, tensor_value in original_state_dict.items():
        # The transformer implementation gives the model a unique name, rather than overwiriting 'roberta'
        if tensor_key.startswith("""roberta."""   ):
            _UpperCAmelCase        = """roberta_prelayernorm.""" + tensor_key[len("""roberta."""   ) :]

        # The original implementation contains weights which are not used, remove them from the state_dict
        if tensor_key.endswith(""".self.LayerNorm.weight"""   ) or tensor_key.endswith(""".self.LayerNorm.bias"""   ):
            continue

        _UpperCAmelCase        = tensor_value

    _UpperCAmelCase        = RobertaPreLayerNormForMaskedLM.from_pretrained(
        pretrained_model_name_or_path=_A       ,   config=_A       ,   state_dict=_A   )
    model.save_pretrained(_A   )

    # convert tokenizer
    _UpperCAmelCase        = AutoTokenizer.from_pretrained(_A   )
    tokenizer.save_pretrained(_A   )


if __name__ == "__main__":
 a       :						Tuple       		=		argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '''--checkpoint-repo''',
     default=None,
     type=str,
     required=True,
     help='''Path the official PyTorch dump, e.g. \'andreasmadsen/efficient_mlm_m0.40\'.''',
 )
 parser.add_argument(
     '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
 )
 a       :						Union[str, Any]       		=		parser.parse_args()
 convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path)